Experiment Outcome &Literature Review. Presented by Fang Liyu

Transcription

1 Experiment Outcome &Literature Review Presented by Fang Liyu

2 Experiment outcome 1. Data from JD Sample size: 1) Data contains 3325 products in 8 days 2) There are missing values in each data sheet on average. What s new? 1) We have added the new independent variable (search interest) in our model, here we use baidu_adv to replace the search interest. 2) We choose two different variables to represent rating : rating and rating with pic respectively.

3 First, we use the traditional rating to do the experiment

4 Experiment in Random Effets. xtreg rank rating num_review price baidu_adv releasedays Random-effects GLS regression Number of obs = Group variable: product Number of groups = 2800 R-sq: within = Obs per group: min = 1 between = avg = 5.6 overall = max = 8 Wald chi2(5) = corr(u_i, X) = 0 (assumed) Prob > chi2 = rank Coef. Std. Err. z P> z [95% Conf. Interval] rating num_review price baidu_adv releasedays _cons sigma_u sigma_e rho (fraction of variance due to u_i)

5 Experiment in Fixed Effets. xtreg rank rating num_review price baidu_adv releasedays, fe Fixed-effects (within) regression Number of obs = Group variable: product Number of groups = 2800 R-sq: within = Obs per group: min = 1 between = avg = 5.6 overall = max = 8 F(5,13003) = corr(u_i, Xb) = Prob > F = rank Coef. Std. Err. t P> t [95% Conf. Interval] rating num_review price baidu_adv releasedays _cons sigma_u sigma_e rho (fraction of variance due to u_i) F test that all u_i=0: F(2799, 13003) = Prob > F =

6 . hausman FE RE, constant sigmamore Hausman test Coefficients (b) (B) (b-b) sqrt(diag(v_b-v_b)) FE RE Difference S.E. rating num_review price baidu_adv releasedays _cons b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(6) = (b-b)'[(v_b-v_b)^(-1)](b-b) = 6.21 Prob>chi2 = (V_b-V_B is not positive definite) Insignificant P value, there s no difference in choosing random effects or fixed effects

7 Then, we use the new rating with pic as the dependent variable to do the experiment again.

8 Experiment in Random Effects. xtreg rank rating_pic num_review price baidu_adv releasedays Random-effects GLS regression Number of obs = Group variable: product Number of groups = 2278 R-sq: within = Obs per group: min = 1 between = avg = 5.6 overall = max = 8 Wald chi2(5) = corr(u_i, X) = 0 (assumed) Prob > chi2 = rank Coef. Std. Err. z P> z [95% Conf. Interval] rating_pic num_review price baidu_adv releasedays _cons sigma_u sigma_e rho (fraction of variance due to u_i)

9 Experiment in Fixed Effects. xtreg rank rating_pic num_review price baidu_adv releasedays, fe Fixed-effects (within) regression Number of obs = Group variable: product Number of groups = 2278 R-sq: within = Obs per group: min = 1 between = avg = 5.6 overall = max = 8 F(5,10549) = corr(u_i, Xb) = Prob > F = rank Coef. Std. Err. t P> t [95% Conf. Interval] rating_pic num_review price baidu_adv releasedays _cons sigma_u sigma_e rho (fraction of variance due to u_i) F test that all u_i=0: F(2277, 10549) = Prob > F =

10 . hausman FE RE, constant sigmamore Hausman test Coefficients (b) (B) (b-b) sqrt(diag(v_b-v_b)) FE RE Difference S.E. rating_pic num_review price baidu_adv releasedays _cons b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(6) = (b-b)'[(v_b-v_b)^(-1)](b-b) = Prob>chi2 = (V_b-V_B is not positive definite) Significant P value, so we choose the fixed effects model.

11 Experiment outcome 2. Data from Amazon Sample size: 1) Data contains 2920 products in 21 days 2) There are about 7000 missing values in each data sheet on average.

12 Experiment in Random Effects. xtreg rank rating num_review price Random-effects GLS regression Number of obs = Group variable: product Number of groups = 2920 R-sq: within = Obs per group: min = 1 between = avg = 13.4 overall = max = 21 Wald chi2(3) = corr(u_i, X) = 0 (assumed) Prob > chi2 = rank Coef. Std. Err. z P> z [95% Conf. Interval] rating num_review price _cons sigma_u sigma_e rho (fraction of variance due to u_i)

13 Experiment in Fixed Effects. xtreg rank rating num_review price, fe Fixed-effects (within) regression Number of obs = Group variable: product Number of groups = 2920 R-sq: within = Obs per group: min = 1 between = avg = 13.4 overall = max = 21 F(3,36325) = corr(u_i, Xb) = Prob > F = rank Coef. Std. Err. t P> t [95% Conf. Interval] rating num_review price _cons sigma_u sigma_e rho (fraction of variance due to u_i) F test that all u_i=0: F(2919, 36325) = Prob > F =

14 . hausman FE RE, constant sigmamore Hausman test Coefficients (b) (B) (b-b) sqrt(diag(v_b-v_b)) FE RE Difference S.E. rating num_review price _cons b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(4) = (b-b)'[(v_b-v_b)^(-1)](b-b) = Prob>chi2 = (V_b-V_B is not positive definite) Significant P value, so we choose the fixed effects model.

15 Different types of ewom 1) discussion forums 2) UseNet groups 3) product reviews 4) blogs 5) social networking sites (SNS)

16 Characteristics of ewom 1) Enhanced volume 2) Dispersion 3) Persistence and observability 4) Anonymity and deception 5) Salience of valence 6) Community engagement

17 4 quadrants of ewom Quadrant 1: Antecedents of ewom Senders (Why Do People Talk Online?) Quadrant 2: Consequences for ewom Senders (What Happens to the Communicator?) Quadrant 3: The Antecedents of the Receiver (Why do people listen?) Quadrant 4: The Consequences to the Receiver (The Power of ewom)

18 The framework of ewom ewom Characteristics C1. Enhanced Volume C2. Dispersion C3. Persistence and Observability Quadrant 1: Antecedents of ewom Senders What We Know Self-Enhancement, Consumer Psychographics, Product/Retailer Performance, Altruism/Concern for Others, Need for Social Interaction Relevant ewom Characteristics: C1, C3, C6 What We Need to Know RQ1, RQ2 Quadrant 2: Consequences to the Senders What We Know Enhanced Product Learning, Impression Management Social Capital and Reputation Relevant ewom Characteristics: C3, C4, C6 What We Need to Know RQ3 Research Questions RQ1- How can firms foster higher-quality reviews and reviewers? RQ2- What is the potential of visual ewom? RQ3- How does ewom affect consumer engagement? RQ4- Are there latent or counterintuitive aspects of ewom seeking? C4. Anonymity and Deception C5. Salience of valence C6. Community Engagement Quadrant 3: Antecedents of the Receiver What We Know Search/Evaluation Efforts, Risk Reduction, Social Assurance Leisure Activity Relevant ewom Characteristics: C1, C2, C3, C4 What We Need to Know RQ4, RQ5, RQ6 Quadrant 4: Consequences to the Receiver What We Know Product ROI, Willingness-to-Pay, Trust and Loyalty Relevant ewom Characteristics: C1, C2, C3, C5 What We Need to Know RQ7, RQ8, RQ9, RQ10, RQ11 RQ5- How do consumers process the textual content in ewom messages? RQ6- How does ewom differ cross-culturally? RQ7- What are the disaggregate effects on receivers? RQ8- How does trust change the power of ewom? RQ9- How does ewom change the consumer decision journey? RQ10- How does ewom affect service delivery modes and costs? RQ11- How can firms utilize ewom s inherent endogeneity?

19 The track of WOM research 1. consumer-generated information V.S. sellercreated information 2. internal and external WOM 3. multiple WOM sources 4. analyze variations of WOM influence across different WOM sources